scientific-computing

Is there a numerically stable way to compute softmax function below? I am getting values that becomes Nans in Neural network code. np.exp(x)/np.sum(np.exp(y)) The softmax exp( x )/sum(exp( x )) is actually numerically well-behaved. It has only positive terms, so we needn't worry about loss of significance, and the denominator is at least as large as the numerator, so the result is guaranteed to fall between 0 and 1. The only accident that might happen is over- or under-flow in the exponentials. Overflow of a single or underflow of all elements of x will render the output more or less useless.

I have about 500 HDF5 files each of about 1.5 GB. Each of the files has the same exact structure, which is 7 compound (int,double,double) datasets and variable number of samples. Now I want to concatenate all this files by concatenating each of the datasets so that at the end I have a single 750 GB file with my 7 datasets. Currently I am running a h5py script which: creates a HDF5 file with the right datasets of unlimited max open in sequence all the files check what is the number of samples (as it is variable) resize the global file append the data this obviously takes many hours, would you

For my current project in C++ / Qt I need a library (LGPL is preferred) which can calculate a spectrogram from a signal ( basically an array of doubles ). I already use Qwt for the GUI part. Any suggestions? Thanks. It would be fairly easy to put together your own spectrogram. The steps are: window function (fairly trivial, e.g. Hanning) FFT (FFTW would be a good choice but if licensing is an issue then go for Kiss FFT or similar) calculate log magnitude of frequency domain components (trivial: log(sqrt(re * re + im * im)) David Cary "How do I create a frequency vs time plot?" lists several

Background Last year, I did an internship in a physics research group at a university. In this group, we mostly used LabVIEW to write programs for controlling our setups, doing data acquisition and analyzing our data. For the first two purposes, that works quite OK, but for data analysis, it's a real pain. On top of that, everyone was mostly self-taught, so code that was written was generally quite a mess (no wonder that every PhD quickly decided to rewrite everything from scratch). Version control was unknown, and impossible to set up because of strict software and network regulations from